Q1. Based on the regression equation, with every 1000 unit increase in income, the monthly revenue will:
- Decrease by 2.2 unit
- Decrease by 81.6 unit
- Increase by 2.2 unit
- Increase by 81.6 unit
Q2. Looking at the Least Square Method plot, what will be your conclusion?
- The data is random
- There is a linear relation between the two variables
- The data has autocorrelation
- The data is normal
Q3. Do you agree the fact that the estimate of the target variable is unbiased, i.e. average residuals is almost zero?
- Don’t Agree
- Agree
Q4. In this simulation we tried to predict Annual Income of a customer through his/her average monthly revenue. True or False?
- FALSE
- TRUE
Q5. What is the percentage of variation explained by the model?(Mark the closest value)
- 40%
- 39%
- 60%
- 61%
Q6. Given other factors unchanged, if a flight departs 10 mins late, the flight’s expected delay in arrival is:
- Delayed by 9 seconds
- Early by 9 seconds
- Delayed by 9 minutes
- Early by 9 minutes
Q7. Find the odd one out (based on variable significance):
- Departure Delay
- Taxi Out Time
- Air Time
- Taxi In Time
Q8. The most important variable to predict arrival delay (as per the model) is:
- Air Time
- Mid of Week Flag
- Distance
- Departure Delay
Q9. Find the odd one out (based on variable type):
- Taxi Out Time
- Distance
- Departure Delay
- Air Time
Q10. What is the MAPE of the model? (Select the closest value)
- 0.308
- 0.196
- 0.087
- 0.219
Q11. Did the model rank ordered with respect to predicted value of average fare?
- TRUE
- FALSE
Q12. What percentage of observations have absolute percentage deviation less than 25%? (Select the closest value)
- 0.59
- 0.38
- 0.62
- 0.21
Q13. How much improvement we got from using the variable transformation? (Mark the closest value)
- R-square dropped little more than 1%
- R-square increased little less than 1%
- R-square increased little more than 1%
- R-square dropped little less than 1%
Q14. From the final model what we can infer about the predictor Retail Establishment?
- It has an insignificant impact at Retail Sales
- It has a positive directional impact at Retail Sales
- It has a negative directional impact at Retail Sales
- None of the above
Q15. From the final model what we can infer about the predictor Income?
- It has a negative directional impact at Retail Sales
- It has an insignificant impact at Retail Sales
- None of the above
- It has a positive directional impact at Retail Sales
Q15. Which variable did you identified to cause overfitting in the model?
- Market-share of lowest price airline
- Average Weekly Passenger
- Market-share of leading airline
- Average Fare of Leading Airline
Q16. What was the drop in model R-square post removal of overfitting? (select the closest one)
- 43% from 97%
- 44% from 96%
- 43% from 96%
- 44% from 97%
Q17. Which variable came insignificant at 99% at the first run?
- Lowest Price
- None of the above
- Average Weekly Passengers
- Distance
